Data-Intensive architecture for scientific knowledge discovery

Atkinson, Malcolm and Liew, Chee Sun and Galea, Michelle and Martin, Paul and Krause, Amrey and Mouat, Adrian and Corcho, Oscar and Snelling, D. (2012). Data-Intensive architecture for scientific knowledge discovery. "Distributed And Parallel Databases", v. 30 (n. 5-6); pp. 307-324. ISSN 0926-8782. https://doi.org/10.1007/s10619-012-7105-3.

Description

Title: Data-Intensive architecture for scientific knowledge discovery
Author/s:
  • Atkinson, Malcolm
  • Liew, Chee Sun
  • Galea, Michelle
  • Martin, Paul
  • Krause, Amrey
  • Mouat, Adrian
  • Corcho, Oscar
  • Snelling, D.
Item Type: Article
Título de Revista/Publicación: Distributed And Parallel Databases
Date: October 2012
ISSN: 0926-8782
Volume: 30
Subjects:
Freetext Keywords: Knowledge discovery, Workflow management system, Descubrimiento de conocimientos, Sistema de gestión del flujo de trabajo.
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

This paper presents a data-intensive architecture that demonstrates the ability to support applications from a wide range of application domains, and support the different types of users involved in defining, designing and executing data-intensive processing tasks. The prototype architecture is introduced, and the pivotal role of DISPEL as a canonical language is explained. The architecture promotes the exploration and exploitation of distributed and heterogeneous data and spans the complete knowledge discovery process, from data preparation, to analysis, to evaluation and reiteration. The architecture evaluation included large-scale applications from astronomy, cosmology, hydrology, functional genetics, imaging processing and seismology.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7215024ADMIREUnspecifiedAdvanced Data Mining and Integration Research for Europe

More information

Item ID: 16379
DC Identifier: http://oa.upm.es/16379/
OAI Identifier: oai:oa.upm.es:16379
DOI: 10.1007/s10619-012-7105-3
Official URL: http://link.springer.com/article/10.1007%2Fs10619-012-7105-3
Deposited by: Memoria Investigacion
Deposited on: 11 Jul 2013 14:57
Last Modified: 31 Oct 2014 12:00
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